IEEE VAST CHALLENGE 2022

Entry Name: “RLR-SMU-C3”
VAST Challenge 2022
Challenge 3

Team Members:

Raveena Chakrapani, Singapore Management University, raveenac.2021@mitb.smu.edu.sg PRIMARY
Leslie Long Nu, Singapore Management University, nu.long.2021@mitb.smu.edu.sg
Raunak Kapur, Singapore Management University, raunakk.2021@mitb.smu.edu.sg

Student Team: YES

Tools Used:

• RStudio
  
• Excel
  
• JMP Pro

Approximately how many hours were spent working on this submission in total? 60

May we post your submission in the Visual Analytics Benchmark Repository after VAST Challenge 2022 is complete? YES

Video

video

Consider the financial status of Engagement’s businesses and residents. Use visual analytics to analyze the available data and develop responses to the questions to be provided. In addition, prepare a video that shows how you used visual analytics to solve this challenge.

Questions

1 – Over the period covered by the dataset, which businesses appear to be more prosperous? Which appear to be struggling? Describe your rationale for your answers. Limit your response to 10 images and 500 words.

The sparkline plot of the businesses’ monthly revenue trend gives us an overview of the prosperity of the businesses in town, namely pubs and restaurant. May 2023’s records are excluded from the data as the records do not include a full month.

The following plot shows that all pubs’ business were at their peaks in the first month of the study, Mar 2022, and faced drastic decline thereafter. Business in Mar 2022 is up to 2x of the average monthly revenue in the duration of the study.

Business	Min	Max	Average	Monthly Revenue
Pub 1342	42028.902	97796.03	53127.99
Pub 1343	21104.324	59168.76	28886.23
Pub 1344	37705.965	89767.68	46226.17
Pub 1798	25373.796	56899.34	31047.79
Pub 1799	16411.683	38925.96	20509.12
Pub 1800	23601.629	52171.44	29038.29
Pub 442	13186.967	31726.98	16155.54
Pub 443	9966.812	25181.48	12274.15
Pub 444	14515.211	28820.72	17108.64
Pub 892	19242.812	49720.06	23836.56
Pub 893	23315.619	59401.47	30767.57
Pub 894	17238.772	42046.89	20991.35

As Mar 2022’s revenue is significantly higher than the rest of the months, the following plot is the monthly revenue sparkline plot excluding Mar 2022, which takes a closer look on the trend in the subsequent months.

Rank	Business	Min	Max	Average	Monthly Revenue
high	Pub 1342	42028.902	60783.30	49691.99
high	Pub 1344	37705.965	52582.48	42876.82
high	Pub 1798	25373.796	36352.46	29059.20
low	Pub 442	13186.967	17994.15	14957.74
low	Pub 443	9966.812	13654.70	11281.28
low	Pub 444	14515.211	19148.46	16207.71
medium	Pub 1343	21104.324	33827.26	26556.80
medium	Pub 1799	16411.683	24175.16	19092.44
medium	Pub 1800	23601.629	35091.13	27258.82
medium	Pub 892	19242.812	28047.76	21845.52
medium	Pub 893	23315.619	33036.13	28564.96
medium	Pub 894	17238.772	23743.19	19371.69

Excluding records in Mar 2022, there are three types of trends for Pubs:

• Pubs see an overall decreasing trend in revenue, but have a relative larger business size, so might be able to still sustain: Pub 1342, 1798, 1799, 1800, 892

• Pubs see a slight trend of recovering as Revenue in Apr 2023 is slightly higher than the average monthly revenue, however, more data is required to validate if the trend persists. These are Pub 1343, 1344, 893, 894

• Pubs that are most struggling as with small overall business size and decreasing revenue: 442, 443, 444

• Overall, Pubs that are most struggling due to small overall business size and decreasing revenue: 442, 443, 444

The revenue for struggling and slight recovering pubs by day of the week and by week number is also plotted, to look at the week by week business performance. Generally, pubs are seen to have higher revenue during the weekends, as well as during the new years weekend (2023, week1).

Restaurants

For restaurants, the trend is slightly different. We group restaurant businesses into three groups as well:

• Peak in Mar 2022 and decreasing trend thereafter: Restaurant 1801, 1802, 1803, 446, 448, 895, 899, 1347, 1349,
• Lowe Revenue in Mar 2022 and shown growth in subsequent months: Restaurant 897, 1348, 1345, 1804,
• Fluctuating revenue and relatively stable business: Restaurant 1805, 447, 449, 1346, 445, 898

Considering their business sizes, the most struggling restaurants are 1347 and 1349, followed by other restaurants with decreasing revenue. Restaurants 897 is most prosperous as it has relatively bigger business size and is still growing, followed by other growing restaurants.

Rank	Business	Min	Max	Average	Monthly Revenue
high	Restaurant 1801	16287.56	20486.18	17989.363
high	Restaurant 1805	13483.08	15842.06	14817.892
high	Restaurant 447	12643.98	14111.48	13603.306
high	Restaurant 449	13439.30	14982.52	14423.340
high	Restaurant 897	11099.52	13000.32	12178.286
low	Restaurant 1346	2304.90	2638.35	2497.532
low	Restaurant 1347	2588.67	3073.77	2820.510
low	Restaurant 1348	2521.08	2897.55	2751.011
low	Restaurant 1349	1497.78	1831.68	1632.362
low	Restaurant 445	2739.80	3023.05	2911.957
medium	Restaurant 1345	7722.10	8892.40	8376.407
medium	Restaurant 1802	4963.16	6731.00	5501.640
medium	Restaurant 1803	3284.25	3932.40	3567.311
medium	Restaurant 1804	5754.24	6837.60	6304.377
medium	Restaurant 446	4520.28	5487.72	5050.764
medium	Restaurant 448	8115.58	11045.98	9120.289
medium	Restaurant 895	7854.36	12591.84	9143.546
medium	Restaurant 896	7606.28	8719.68	8224.510
medium	Restaurant 898	3388.66	3861.30	3648.190
medium	Restaurant 899	9836.65	13712.55	11044.943

While some restaurants show higher revenue/customer traffic during the weekends and some during weekdays, most restaurants see high revenue in New Years Weekend (2023 Week1). It is clear that 1347 and 1349 are the most struggling restaurants, with overall low revenue and some days with nil revenue.

On the other hand, although 1348’s overall revenue is not low, as the business is growthing, it is identified as a prosperous restaurant.

2 – How does the financial health of the residents change over the period covered by the dataset? How do wages compare to the overall cost of living in Engagement? Are there groups that appear to exhibit similar patterns? Describe your rationale for your answers. Limit your response to 10 images and 500 words.

How does the financial health of the residents change over the period covered by the dataset

Are we financially fit?

The dashboard helps us understand the Earning and Expenditure pattern of the participants in the last 15 months. We can notice here that 131 participants have moved from the city in the period as they are not recorded. On scrolling through each and every participant, we see a sharp peak in the Expense category which denotes the rent payment for the shelter in the month of March. This may include the annual maintainance as well along with the rent.

On choosing joviality as one of the filters, we can see those whose cost of living is nearing a straight line over the months are happier than the ones whose cost of living fluctuate

The cost of living may not stay constant when you have kids in the house

How do wages compare to the overall cost of living in Engagement?

Observations:

• The income distribution in Engagement city tells us that 50% of the population takes in 25% of the city’s income.
• The absolute line gives us an ideal scenario where the 50% of the population takes in 50% of the city’s income

How much do they spend?

Averaging the proportion in the past 15 months, we can see that participants who spend a lot on education, spend less on other categories which goes on to prove that raising a kid is expensive in the city. Similarly those who spend a lot on shelter, have no such responsibilities and as a result can afford to pay high rent.

As it can be noticed here, the participants tend to spend more on recreation than food. Studying the weekday and weekend patterns, we can see an increase in the expense during the weekend. But overall, even if its a weekday or a weekend, one wants to balance out between work and play and ensures that they do not spend all their time working but spend a considerable amount of time enjoying and refreshing themselves.

Are there groups that appear to exhibit similar patterns?

How similar are the groups

• Over the months we can see that the cost of living of members in F and J had a more ‘expensive’ lifestyle compared to other group members
• Members in A,G,H saw a fluctuation in their cost of livings over the months.
• Members in E spent the least over the 15 months period
• The sharp peak at the start of every month denotes rent paid

Based on categories as cluster variables, we were able to segment participants to understand how their spending patterns have been. We have seen that participants who spend more on education, tend to spend more on recreation as well signifying a good mental health balance.

3 – Describe the health of the various employers within the city limits. What employment patterns do you observe? Do you notice any areas of particularly high or low turnover? Limit your response to 10 images and 500 words.

Well, from the given data, the health of the employers can be compared by no. of jobs and wage they provide. Given the fact that the employers vary in no. of jobs they provide , who pays their employees high wage is also a crucial criteria while determining the employers financial health. First let us look how employers are widespread across the town.



It is noticed that Central region the employers in the central region offers quite a lot of jobs followed by North-west, East and South parts of the town as the treemap is constructed in a way where he size of the block represents the no. of job poistions in the company and the color indicates the average wage per employee. So, we can infer that employer 1308 in Central, 1304 in North-west, 420 in East offer high wage to their employees.



It is observed that Employers with Ids 383, 429 who have highest no. of jobs are paying well their employees. Also employers with Ids 848, 866 are even more good in terms of pay but they offeronly 2 jobs. Keeping all these employers in the healthy pedestal, employers like 1301, 1762 are really financially unhealthy as they offer already provide less no. of jobs and they pay their employees so low.

It is clear that employers with Ids 1737, 1771 provide high wages for employees with Low Educational qualification. Similarly employers with Ids 424, 1777 provide high wages for employees with High School or College Educational qualification. Employers with Ids 1304, 1782 provide high wages for employees with Bachelors Educational qualification and employers with Ids 428, 865 provide high wages for employees with Graduate Educational qualification.



Employers like 407, 411 lost 3 employees in the overall timeline of this data collection whereas employers with Ids 389, 397 have gained 14 and 13 employees respectively. But there are possibilities when a employee left a job,many others can join the same employer. So,considering that edge case, the below graph shows overall turnover for all the employers in the town.



It is well seen that the employers in the west part of the town has seen employee growth compared to other parts of the city.



Calculating the difference of employees joined an organisation and no. of employees left an organisation, the employer with id 1756, 1757 and 389 have noticed very low turnover with 14, 13 and 8 employees joining the firm.



Finally, we can see that employer with Id 383 spends the most for wages i.e. total wage of 258/ per hour summing up all the employees followed by employer with Ids 1786 and 877 by paying 238 and 229 respectively for their employees.